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G-estimation for Accelerated Failure Time Models

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Modern Methods for Epidemiology

Abstract

In this chapter we examine the problem of time-varying confounding, and one method (structural nested accelerated failure time models, estimated using and also known as g-estimation) which may be used to overcome it. A practical example is given, and the methodology demonstrated. Cautions as to the use of g-estimation are provided, and alternative methods suggested. Much of the material in this chapter has been published as an application to analysis of a longitudinal study (Tilling et al. Am J Epidemiol 155:710–718, 2002) and as a description of the implementation of these methods in standard statistical software (Sterne and Tilling, Stata J 2:164–182, 2002). The material is used here with permission from the Stata Journal and the American Journal of Epidemiology (Oxford University Press and the Society for Epidemiologic Research).

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Author information

Authors and Affiliations

  1. School of Social and Community Medicine, University of Bristol, Bristol, UK

    Kate Tilling & Jonathan A. C. Sterne

  2. School of Mathematics, University of Bristol, Bristol, UK

    Vanessa Didelez

Authors
  1. Kate Tilling
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  2. Jonathan A. C. Sterne
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  3. Vanessa Didelez
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Corresponding author

Correspondence to Kate Tilling .

Editor information

Editors and Affiliations

  1. Leeds Institute of Genetics, University of Leeds, Division of Biostatistics, Clarendon Way, Worsley Building, Room 8.01, Level 8, Leeds, LS2 9JT, United Kingdom

    Yu-Kang Tu

  2. Leeds Institute of Genetics, University of Leeds, Division of Biostatistics, Clarendon Way, Worsley Building, Room 8.01, Level 8, Leeds, LS2 9JT, United Kingdom

    Darren C. Greenwood

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Tilling, K., Sterne, J.A.C., Didelez, V. (2012). G-estimation for Accelerated Failure Time Models. In: Tu, YK., Greenwood, D. (eds) Modern Methods for Epidemiology. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-3024-3_14

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